[R-sig-ME] LME and nonlinearity?
smouksassi at Pharsight.com
Fri Apr 10 01:08:54 CEST 2009
Another possible way to diagnose the need of a non-linear model is to use a spline then replace it with more meaningful parametric model according to its shape.
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of John Maindonald
Sent: 2009-04-09 18:51
To: Balázs Lestár
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] LME and nonlinearity?
Additional to the comments below: Think/check also whether
transformation of one or more of the variables (log transformation?)
makes the relationship more nearly linear.
On 10/04/2009, at 7:13 AM, Balázs Lestár wrote:
> Dear All,
> I have a mixed model (LME), but one of my explanatory variables is
> not linearly related to the dependent variable.
> 1.) Somebody told me, to make a 2 or 3 level factor from the
> continuous variable. (I wouldn't prefer this)
In general, this makes poor use of the information in the data. You
> 2.) I saw in some statistical books that in these cases, I have
> to use in the model the quadratic term of the variable. (but the AIC
> is much greater than with the factorized variable)
> Is that possible, to use a poly() function in the lme? (this model
> seems to be the best, based on AIC).
> I'm a bit confused, 'cause the LME supposes linear relation between
> variables. Isn't it right?
Linear models are linear in the parameters. They can model highly
> 3.) Do I need a non-linear model?
Only if you need a model that is non-linear in the parameters.
Without checking out your data and model, one cannot say.
John Maindonald email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473 fax : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.
> Which solution is the best?
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